Independent representations of limb axis length and orientation in spinocerebellar response components.
Independent representations of limb axis length and orientation in spinocerebellar response components.
复制标题
脊髓小脑反应成分中肢体轴长度和方向的独立表示。
DOI：
10.1152/jn.00022.2001
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Rankin,AM
中科院分区：
文献类型：
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作者：
Poppele,RE;Bosco,G;Rankin,AM
背侧脊髓小脑束（DSCT）神经元将感觉信号传递到小脑，编码全局后肢参数，如后肢终点位置及其运动方向。在这里，我们使用人口分析的方法来进一步研究的特点，DSCT神经元反应在连续运动的后足。我们使用机器人移动麻醉猫的后爪通过一个步骤的轨迹或一个数字-8人行道在parasynchronous平面。从82个细胞的细胞外记录转换为周期直方图提供了基础的主成分分析，以确定的DSCT运动反应的共同特点。五个主成分（PC）占整个单位的波形总方差的80%左右。前两个PC约占60%的方差，它们在样本中具有高度稳健性。我们研究的反应和肢体运动学参数之间的关系，通过相关的PC波形与波形的关节角和肢体轴轨迹，使用多元线性回归模型。每个PC波形至少可以部分地通过与关节角度轨迹的线性关系来解释，但是除了第一个PC之外，它们需要多个角度。然而，肢体轴参数与第一和第二PC波形两者更密切相关。事实上，以肢体轴长度和方向轨迹作为预测因子的线性回归模型解释了两种PC中94%的方差，并且每种模型都与位置和速度的特定线性组合相关。第一PC与肢体轴方向和方向速度轨迹相关，而第二PC与长度和长度速度轨迹相关。发现这些组合对应于肌梭反应的动力学。前两个PC也是最具代表性的数据集，因为约一半的DSCT响应可以通过这两个PC的加权线性组合至少占85%。高阶PC无关的肢体轴轨迹，而占不同的动态组件的响应。研究结果表明，一个明确的和独立的代表肢体轴的长度和方向可能存在于最低水平的感觉处理在脊髓。
Dorsal spinocerebellar tract (DSCT) neurons transmit sensory signals to the cerebellum that encode global hindlimb parameters, such as the hindlimb end-point position and its direction of movement. Here we use a population analysis approach to examine further the characteristics of DSCT neuronal responses during continuous movements of the hind foot. We used a robot to move the hind paw of anesthetized cats through the trajectories of a step or a figure-8 footpath in a parasagittal plane. Extracellular recordings from 82 cells converted to cycle histograms provided the basis for a principal-component analysis to determine the common features of the DSCT movement responses. Five principal components (PCs) accounted for about 80% of the total variance in the waveforms across units. The first two PCs accounted for about 60% of the variance and they were highly robust across samples. We examined the relationship between the responses and limb kinematic parameters by correlating the PC waveforms with waveforms of the joint angle and limb axis trajectories using multivariate linear regression models. Each PC waveform could be at least partly explained by a linear relationship to joint-angle trajectories, but except for the first PC, they required multiple angles. However, the limb axis parameters more closely related to both the first and second PC waveforms. In fact, linear regression models with limb axis length and orientation trajectories as predictors explained 94% of the variance in both PCs, and each was related to a particular linear combination of position and velocity. The first PC correlated with the limb axis orientation and orientation velocity trajectories, whereas second PC with the length and length velocity trajectories. These combinations were found to correspond to the dynamics of muscle spindle responses. The first two PCs were also most representative of the data set since about half the DSCT responses could be at least 85% accounted for by weighted linear combinations of these two PCs. Higher-order PCs were unrelated to limb axis trajectories and accounted instead for different dynamic components of the responses. The findings imply that an explicit and independent representation of the limb axis length and orientation may be present at the lowest levels of sensory processing in the spinal cord.